U.S. patent application number 11/389491 was filed with the patent office on 2006-12-28 for bi-chassis framework.
This patent application is currently assigned to SATYAM COMPUTER SERVICES LIMITED. Invention is credited to Sayed Javeed Ahmed, Kiran N. Cavale, N. Satish Chandra, Krishna G., Gautam Kar, Schandra S. S. V. Muni Kumar, Indira Munjuluri.
Application Number | 20060294153 11/389491 |
Document ID | / |
Family ID | 29254591 |
Filed Date | 2006-12-28 |
United States Patent
Application |
20060294153 |
Kind Code |
A1 |
Kumar; Schandra S. S. V. Muni ;
et al. |
December 28, 2006 |
Bi-chassis framework
Abstract
The present invention provides a system consisting of reusable
components for implementing data warehousing and business
intelligence solutions. The reusable components comprise a data
model component housing an exhaustive pre-built vertical and
business function specific data models and key performance
indicator libraries. The reusable components further comprise a
MetAL component which serves as a key repository of all mappings
between all standard source data systems and vertical function
specific data models and KPIs used in the data model component.
Being compliant with Common Warehouse Metamodel, the system is
extraction and reporting tool neutral. In other words, the
technical and business metadata can be exported to any of the CWM
compliant extraction and reporting tools. The system further
consists of an algorithm to automatically detect version changes in
the standard source systems.
Inventors: |
Kumar; Schandra S. S. V. Muni;
(Hyderabad, IN) ; Munjuluri; Indira;
(Secunderabad, IN) ; Cavale; Kiran N.; (Chennai,
IN) ; Kar; Gautam; (Secunderabad, IN) ;
Chandra; N. Satish; (Hyderabad, IN) ; Ahmed; Sayed
Javeed; (Hyderabad, IN) ; G.; Krishna;
(Hyderabad, IN) |
Correspondence
Address: |
VENABLE LLP
P.O. BOX 34385
WASHINGTON
DC
20043-9998
US
|
Assignee: |
SATYAM COMPUTER SERVICES
LIMITED
Secunderabad
IN
|
Family ID: |
29254591 |
Appl. No.: |
11/389491 |
Filed: |
March 27, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10422998 |
Apr 25, 2003 |
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11389491 |
Mar 27, 2006 |
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60375447 |
Apr 26, 2002 |
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Current U.S.
Class: |
1/1 ; 707/999.2;
707/E17.063; 707/E17.107 |
Current CPC
Class: |
G06F 16/95 20190101;
G06F 16/254 20190101 |
Class at
Publication: |
707/200 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A system consisting of reusable components for implementing data
warehousing (DW) and business intelligence (BI) solutions, said
reusable components comprising: a data model component housing an
exhaustive pre-built vertical and business function specific
generic data models and key performance indicator (KPI) libraries;
a MetAL component serving as the key repository of all mappings
between all standard source data systems and vertical and function
specific data models and KPIs housed in the data model component;
and a component with the ability to export these mappings to any
ETL and reporting tool, making it BI tool neutral and a platform
neutral framework; thereby positioning itself as a technology
neutral platform for organizations implementing data
warehouses.
2. A system consisting of reusable component for implementing data
warehousing (DW) and business intelligence (BI) solutions, said
reusable component comprising a data model housing an exhaustive
pre-built vertical and business function specific generic data
models and key performance indicator (KPI) libraries; said data
model component being provided with a user interface to reconfigure
the data models and KPIs.
3. The system of claim 2, wherein said data model component is
capable of importing/exporting data models using MS Excel.
4. The system of claim 3, wherein the data model component reports
for any given dimensional model.
5. The system of claim 2, wherein the data model supports Star and
Snow Flake schema.
6. The system of claim 2, wherein the data model component supports
Oracle, SQL Server, DB2, Sybase, Teradata, Informix, SAP,
PeopleSoft, Siebel, JDE, BaaN and Mfg Pro.
7. The system of claim 2, wherein the data model component is
capable of joining set of dimensional models for an EDW.
8. The system of claim 2, wherein the data model component provides
access to best practices for time, name and address dimensions.
9. The system of claim 2, wherein the data model component provides
guidelines for handling of slowly changing dimensions, rapidly
changing small dimensions, monster dimensions, degenerate
dimensions, junk dimensions.
10. A system consisting of reusable component for implementing data
warehousing (DW) and business intelligence (BI) solutions, said
reusable component comprising a MetAL component serving as a key
repository of all mappings between all standard source data systems
and vertical and function specific data models and KPIs housed in
the data model component; said MetAL component supports the
metadata for Oracle Applications, SAP, PeopleSoft, Siebel, Oracle,
CRM, Vantive, Clarify, JD Edwards, BaaN, Mfg Pro.
11. The system of claim 10, wherein the MetAL component is capable
of bringing into framework relational and non-relational databases
RDBMS, File Systems, Dbase, Paradox, Btree.
12. A system consisting of reusable component for implementing data
warehousing (DW) and business intelligence (BI) solutions, said
reusable component comprises a MetAL component serving as a key
repository of all mappings between all standard source data systems
and vertical and function specific data models and KPIs housed in
the data model component, said MetAL component having the ability
to export the mappings to ETL Tools-Oracle Warehouse Builder,
Informatica, Sagent, SAS, Acta, Visual Datawarehouse Administrator,
Abinitio, DTS, Data Junction.
13. The system of claim 12, wherein the MetAL component exports the
mapping to leading BI tools, packaged applications, CASE tools,
database and system management tools.
14. The system of claim 12, wherein the user interface is provided
to construct the source to target mappings.
15. A method for implementation and maintenance of projects
comprising the the steps of: analysing the critical success
factors;existing business processes, source data, IT infrastructure
and reporting needs after collecting required information from
business users and IT users; designing in sequence the logical data
modeling and physical database; developing back room services end
user applications, product installation and total time; deploying
physical data base implementation, initial load and validation of
the data base, system testing, transition and user training; and
maintaining and supporting the system.
16. A system consisting of reusable components for implementing
data warehousing (DW) and business intelligence (BI) solutions
substantially as herein described.
Description
[0001] The present invention relates to a system consisting of
reusable components for implementing data warehousing (DW) and
business intelligence (BI) solutions. The system is a combination
of various components that would enable to have an access to the
best practices as well as certain domain and business function
specific data models, components, applications that enable building
an integrated data warehousing (DW) and business intelligence (BI)
infrastructure faster as well as enable their easy maintenance and
support.
[0002] Further, the system provides an enriched framework which
assists in applying certain unique concepts, experiences,
philosophies and pre-packaged solutions to all its data warehousing
and business intelligence engagements.
[0003] The data warehouse is a subject-oriented, integrated,
time-variant, non-volatile collection of data used to support the
strategic decision-making process for the enterprise. The data
warehouse supports online analytical processing (OLAP), data mining
and other statistical/analytical and related decision support
applications, the functional and performance requirements of which
are quite different from those of the online transactional
processing (OLTP).
[0004] Thus, the unique combination of DW and BI addresses every
requirement. The components play a very vital role in ensuring the
achievement of objectives relating to DW and BI engagements.
[0005] The known systems available in the market such as
Informatica Analytical Applications and Business Objects
Application Foundation to just name a few. Informatica Analytical
Applications offers pre-built data models for customer analytics,
financial analytics, HR analytics and supply chain analytics. The
system also offers the functionality of data integration and
information delivery thorugh Informatica PowerCentre and
Informatica Power Analyser respectively, which are two separate
products. Similarly, Business Objects Application Foundation is a
framework for delivering analytical applications. It comes with
pre-built matrices, business rules which enable various kinds of
analysis apart from offering the functionality to perform
predictive analysis and statistical process control.
[0006] The known systems however suffer from certain
deficiencies.
[0007] 1. They are products which need to be purchased.
[0008] 2. These products are tied to their own extraction and
information delivery tools, which means customers have to purchase
these tools separately
[0009] 3. These products do not come with pre-built data mappings
with any of the standard data sources.
[0010] 4. Lack of pre-built data mappings also means that any
version changes in any of the standard data sources would require
re-mapping the data sources with the target data models.
OBJECTS OF THE INVENTION
[0011] An object of the present invention is therefore to provide a
system to be utilised in DW and BI service engagements including
plan, build and operate and across specialised service
offerings.
[0012] Another object of the present invention is to enable DW and
BI to build an asset bass of reusable objects.
[0013] Yet another object of the present invention is to increase
DW and BI engagement productivity.
[0014] A further object of the present invention is to ensure
uniformity in approach to all DW and BI engagements.
[0015] Still another object of the present invention is to provide
a structured channel for capturing engagement knowledge as well as
to act as a self-reinforcing feedback loop.
[0016] Still further object of the present invention is to develop,
test and incorporate applications that are necessary to plug gaps,
in a cost effective manner, in the available set of tools and
technologies.
SUMMARY OF THE INVENTION
[0017] The system of the present invention using reusable
components solves the above deficiencies. The system uses reusable
components consisting of pre-built vertical specific data models
and key performance indications (KPIs) and pre-built maplets
linking standard source systems to the vertical specific KPIs, to
aid faster and cost effective implementation of data warehousing
and business intelligence projects. While the system's data model
component houses the vertical specific data models and KPIs, the
metadata component houses the technical and business metadata along
with the associated mappings. Being compliant with the common
warehouse metamodel (CWM), the system is extraction and reporting
tool neutral. In other words, the technical and business metadata
can be exported to any of the CWM compliant extraction and
reporting tools available in the market. It also consists of an
algrorithm to automatically detect version changes in the standard
source systems.
[0018] The system of the present invention seeks to solve the
deficiences in the products available in the market in the
following ways: [0019] It is a royalty free framework for
implementation of data warehousing and business intelligence
projects, thereby eliminating the need to buy the product. The data
models would remain with the customers after the implementation.
[0020] The metadata residing in the system can be exported to any
common warehouse metamodel (CWM) compliant extraction or
information delivery tool, thereby making the system tool neutral.
The system can make use of the existing extraction or information
delivery tool. [0021] It comes with pre-built mappings between the
fields in standard data sources and the pre-built data models and
KPIs. The technical and business metadata of the standard data
sources as well as the target KPIs are pre-mapped in the system,
thereby eliminating the need to create mapping afresh. [0022] The
system comes along with an in-built algorithm to take care of
version changes in the standard data sources.
[0023] Thus the present invention provides a system consisting of
reusable components for implementing data warehousing (DW) and
business intelligence (BI) solutions, said reusable components
comprising a data model component housing an exhaustive pre-built
vertical and business function specific generic data models and key
performance indicator (KPI) libraries; a MetAL component serving as
a key repository of all mappings between all standard source data
systems and vertical and function specific data models and KPIs
housed in the data model component; and a component with the
ability to export these mappings to any ETL and reporting tool,
making it BI tool neutral and a platform neutral framework; thereby
positioning itself as a technology neutral platform for
organizations implementing data warehouses.
[0024] The data model component houses exhaustive pre-built
vertical and business function specific generic data models and key
performance indicator libraries.
[0025] The MetAL component serves as a key repository of all
mappings between all standard source data systems and the vertical
and function specific data models and KPIs, housed in the data
model component.
[0026] The MetAL component contains the metadata of the various
versions of all standard source systems and the metadata of the
pre-built data models and KPIs in CWM format. It also contains the
associated mappings between these two sets of metadata.
[0027] Thus, the MetAL component has four different engines. The BI
configuration engine houses tie technical and business metadata of
pre-built KPI libraries. The data sources engine of the MetAL
component houses the technical and business metadata of the
standard source systems. The integration engine contains the
mappings between the metadata in the data sources engine and the
metadata of the BI configuration engine. The mapping export engine
exports the metadata to any extraction or information delivery
tool.
[0028] Some of the additional features of these components are
given below: [0029] The data model component is provided with user
interface for reconfiguring the data models and KPIs. [0030] It is
able to import/export data models using MS Excel [0031] It reports
for any given dimensional model [0032] The data model component
supports Star and Snow Flake schema. It also supports Oracle, SQL
Server, DB2, Sybase, Teradata, Informix, SAP, PeopleSoft, Siebel,
JDE, BaaN, MfgPro. [0033] It is able to join set of dimensional
models for an EDW. [0034] The data model component provides access
to best practices for time, name and address dimensions. [0035] It
provides guidelines for handling of slowing changing dimensions,
rapidly changing small dimensions, monster dimensions, degenerate
dimensions, junk dimensions.
[0036] The MetAL component is able to support the metadata for
Oracle applications, SAP, People Soft, Siebel, Oracle CRM, Vantive,
Clarify, JD Edwards, BaaN, MfgPro.
[0037] It can export the mappings to ETL Tools-Oracle Warehouse
Builder, Informatica, Sagent, SAS, Acta, Visual Datawarehouse
Admininstrator, Abinitio, DTS, Data junction.
[0038] The MetAL component is also able to export mappings to
leading BI tools, packaged applications, CASE tools, database and
system management tools.
[0039] It is able to bring into framework relational and
non-relational databases RDBMS, File Systems, Dbase, Paradox,
Btree.
[0040] The MetAL component is provided with user interface to
construct source to target mappings.
[0041] The system stores its reusable components in the common
warehouse metamodel framework, thereby positioning itself as a
technology neutral platform for organizations implementing data
warehouses.
DETAILED DESCRIPTION OF THE INVENTION
[0042] The system of the present invention is provided in the form
of an application which comprises the following components:
[0043] Data model component
[0044] MetAL component
[0045] Additionally, the system also constitutes certain add on
applications having independent applications and which can be
provided separately.
[0046] The data model component, a part of the overall system of
the present invention, provides for access to pre-packaged data
models, enable their reconfiguration as well as provide aids to
dimensional modeling in the DW and BI context.
[0047] The data model component is organized across verticals and
business functions across these verticals. The data model component
of the system has the following additonal features: [0048] Enable
definitions of vertical and business function specific key
performance measures (KBM) [0049] Means to capture all the possible
dimensions and their respective attributes/properties as well as
establish mappings between the KBM and dimensions [0050] Ability to
generate data model based on definitions of dimensions, measures
and analytical needs. [0051] Built in dimensional models
representing the best practices for enabling comprehensive analysis
relating to particular business functions across verticals [0052]
User interface to reconfigure these standard data models [0053]
Ability to import/export data models using MS Excel [0054] Ability
to generate list of reports for a given dimensional model [0055]
Supports Star scheme and Snow Flake schema [0056] Supports the
following target databases oracle, SQL Server, DB2, Sybase,
Teradata, Informix Redbrick among others. [0057] Ability to join
set of dimensional models for an EDW [0058] Best practices for
time, name and address dimensions [0059] Provides guidelines for
handling of slowly changing dimensions, rapidly changing small
dimensions, monster dimensions, degenerate dimensions, junk
dimensions.
[0060] The list of in-built data models currently available in the
data model component is given in Table 1. TABLE-US-00001 TABLE 1
Function Strategic/Corporate Management/Other Sales &
Marketing& Accounting & Human Domain Analytics After Sales
Service Finance Resources Procurement Operations Manufacturing CPM
Sales & Financial HR Materials Production Distribution, CRM
Management Planning & Control Financial CPM, Claim Analysis,
Sales & Financial HR Materials Production Services Risk
Analysis, Credit Distribution, CRM Management Planning & Card
Analysis, Fraud Control Detection Telecom CPM, Call Analysis, Sales
& Financial HR Materials Operational Chum Management,
Distribution, CRM Management Planning Fraud Detection Retail CPM
Sales & Financial HR Materials Production Distribution, CRM
Management Planning & Control Transportation CPM Sales &
Financial HR Materials Fleet Distribution, CRM Management
Management Utilities CPM Sales & Financial HR Materials
Production Distribution, CRM Management Planning & Control
Automotive CPM Sales & Financial HR Materials Production
Distribution, CRM Management Planning & Control Healthcare CPM
Sales & Financial HR Materials Production Distribution, CRM
Management Planning & Control Public Sector CPM Sales &
Financial HR Materials Production Distribution, CRM Management
Planning & Control Computers & CPM Sales & Financial HR
Materials Production Technology Distribution, CRM Management
Planning & Control Electronic CPM Sales & Financial HR
Materials Production Commerce Distribution, CRM Management Planning
& Control Energy CPM Sales & Financial HR Materials
Production Distribution, CRM Management Planning & Control
Environment CPM Sales & Financial HR Materials Production
Distribution, CRM Management Planning & Control Media & CPM
Sales & Financial HR Materials Production Entertainment
Distribution, CRM Management Planning & Control
[0061] The MetAl component, a part of the system of the present
invention, provides for acquisition, maintenance and movement of
metadata to and from various architecture components in the
enterprise. This component provides for a MetAl database--a
central, shared source of metadata including prepackaged
metadata--enabling reduction in implementation and maintenance
costs and thereby helping customers get more value.
[0062] The MetAl component of the system has the following
additional features: [0063] Ability to capture the metadata of
different versions of enterprise applications, including business
metadata, and store in CWM format. Current set of enterprise
applications supported include oracle applications, SAP,
PeopleSoft, Siebel, Oracle CRM, Vantive, Clarify, JD Edwards, BaaN,
Mfg Pro. [0064] Ability to establish, capture and store metadata
source to target mappings. Currently the MetAL component has
information of pre-built mappings for all the data models listed in
earlier para to all the enterprise applications listed in earlier
para. [0065] Ability to export the mappings to the following ETL
Tools Oracle Warehouse Builder, Informatica, Sagent, SAS, Acta,
Visual Datawarehouse Administrator, Abinitio, DTS, Data Junction.
[0066] Ability to bring into framework relational and
non-relational databases RDBMS, File Systems, Dbase, Paradox,
Btree. [0067] User interface to construct source to target
mappings. [0068] Ability to enable mappings between any combination
of data stores [0069] Metadata in CWM compliant format [0070]
Enable the exchange of metadata across the following tool
categories as well as between the following tool categories: [0071]
Data movement tools that transform and integrate disparate data
types and move data reliably to the warehouse. [0072] Business
intelligence tools that provide end-user access and analysis for
making business decisions. [0073] Business applications that
provide packaged warehouse solutions for specific markets [0074]
CASE tools [0075] Database and systems management tools
[0076] The data warehousing and business intelligence
implementation methodology is a unique full life-cycle methodology
for implementation of data warehousing and business intelligence
solutions covering all the phases. The DW and BI methodology
provides for a structured and uniform approach to all DW and BI
engagements as well as encapsulates the best practices and unique
approach/philosophy towards such engagements. It is a unique
methodology (defined series of steps) for implementation and
maintenance of data warehousing projects. The methodology is
carried out in five stages including requirements analysis, design,
development, deployment and maintenance and support.
[0077] The process flow chart for the implementation methodology is
shown in FIG. 1.
[0078] Requirement analysis stage consists of collecting the
requirements from the business users and IT users in the
organization mainly through interviews. Analysis is done on the
critical success factors, existing business processes, source data,
IT infrastructure, and reporting needs, and the requirments are
documented and prioritized.
[0079] The design stage consists of the following activities. (The
logical data modeling and physical database designing are executed
in sequence. The other activities are executed more or less in
parallel with overlaps/staggered start of activities. Normally back
room processes design and end user applications design are taken up
after technical architecture and database designing have progressed
enough to give inputs to these).
[0080] Technical architecture of the solution is defined based on
the user requirements and the information about the existing
infrastructure. Following are defined as part of the technical
architecture: [0081] Data Warehouse technical architecture [0082]
Capacity plan [0083] Evaluation criteria for products [0084]
Product recommendations [0085] Back up and recovery strategy [0086]
Security strategy [0087] Metadata-collection strategy [0088]
Performance benchmarks for the solution
[0089] A conceptual data model is first developed based on analysis
of source data and the requirements. From the conceptual data
model, the logical data models for the staging area, ODS, data
marts/data warehouse are created as required.
[0090] Physical database design focuses on defining the physical
structures necessary to support the logical data model. Primary
elements of this stage involve defining naming standards and
setting up the database environment. Preliminary indexing and
partition strategies are also determined.
[0091] The back room services include the extraction,
transformation and loading services, metadata services, and
warehouse administration services, if any. This stage involves
design/customization of all back room processes/tools.
[0092] The back room services design includes: [0093] -ETL,
metadata and warehouse administration process design [0094] Source
to target mapping [0095] Prioritizing ETL activities [0096]
Strategies for data quality [0097] Automation of the ETL processes
[0098] Developing program specifications
[0099] The end user application (front room) design involves
design/customization of all data access components/tools (end user
applications), screens and reports.
[0100] End user application design involves: [0101] Identification
and prioritization of reports [0102] End user application modules
and processes [0103] Coding and GUI standards [0104] Report and
screen specifications [0105] Program specifications (if required)
[0106] Interface with external systems
[0107] The development stage consists of the following
activities:
[0108] Back room services development activity involves
coding/scripting for all the back room services including the ETL
processes and warehouse administration processes. Alternately, if
any tool from the market need to be used for extraction and
transformation/scrubbing/cleansing, customization of the same is
carried out.
[0109] In the end user applications development stage, the end user
applications are developed by configuring the data access tools
and/or developing screens and reports. Administrative modules, if
any, are also developed in parallel.
[0110] Product installation involves installation and testing all
hardware and software including ETL tools, servers
(DB/application/web), DBMS, data access tools, metadata management
tools etc.
[0111] Creation and testing of a prototype of the solution. The
scope and nature of the prototype is decided in the requirement
analysis stage.
[0112] Prototyping involves: [0113] Defining the scope for the
prototype [0114] Define acceptance cirteria for the prototype
[0115] Create test cases [0116] Develop the prototype [0117] Review
and test the prototype
[0118] The deployment stage consists of the following
activities:
[0119] Creation of the physical databases for the operational data
store/data mart/data warehouse. Deployment of the backroom and
front room applications (custom-developed) is also done in this
stage.
[0120] Initial load and validation of the database comprising the
extraction, transformation and loading processes are executed for
the initial load of the data warehouse; data validation is done
against the pre-defined data quality norms to ensure the
completeness and correctness of data loaded.
[0121] System tests are conducted as per the System Test plan, and
covering the entire application.
[0122] System test includes: [0123] Volume testing [0124] Stress
testing [0125] Configuration testing [0126] Security testing [0127]
Installability testing [0128] Documentation testing [0129]
Performance testing [0130] Usability testing
[0131] In the transition stage, the complete solution is handed
over to the customer after acceptance tests and user training.
[0132] Transition involves: [0133] Developing user training
material [0134] Setting up user access and security privileges
[0135] Conducting user training [0136] Handing over user
documentation [0137] Performing acceptance testing [0138]
Completion of hand-over
[0139] In the user training stage: [0140] user training needs are
identified during requirement analysis stage [0141] training is
cutomized for different levels/types of users (administrators, IT
personnel, business users etc.) [0142] efforts involved in training
are planned in advance [0143] training materials are prepared
during the design and development stages [0144] training plans are
made and training is performed as per the plan [0145] training
effort is allocated [0146] training is conducted as per the plan,
and monitoring is done to ensure effective training [0147] at least
one "owner" is to be designated for each subject area to interact
and coordinate with the development team [0148] involvement of
business users and IT staff in providing inputs on existing
business processes, infrastructure, and analytical/reporting needs
in the requirement analysis stage [0149] QCB personnel may be
required to spend time in reviewing/approving certain deliverables
as per the review plan [0150] conducting acceptance test.
[0151] The DW and BI methodology has the following features:
[0152] Detailed reference material on the phases, tasks, activities
and all relevant templates
[0153] Detailed aids, guidelines and best practices reflecting
experience, expertise and philosophy relating to such engagements.
These would be especially useful in stages relating to technical
architecting, dimensional modeling, choice of tools, technologies
and approaches, etc.
[0154] Enable systematic documentation relating to the engagement,
structured storage and provide for its import and/or export across
locations
[0155] Audit trail and configuration management
[0156] The unique methodology supports the generic and specialized
solution offersings shown in Table 2. TABLE-US-00002 TABLE 2
Generic Solution Offerings Plan Build Operate Specialized Solution
Offerings Need Rapid Web Data Data Mining ERP/CRM Analysis
Prototyping Enabling Warehousing Intelligence RFP Customization
Maintenance Strategic Enterprise E-Business Preparation &
Migration Enterprise Marketing Intelligence Management Automation
Solution Implementation Performance Analytical Campaign
Architecting & Integration Tuning CRM Management
[0157] Data warehousing and business intelligence engagements
identified gaps with respect to availability of appropriate tools
and technologies vis-a-vis certain specific requirements. As part
of this philosophy to constantly enrich the base of reusable
components, the following add on applications can be included in
the system of the present invention.
[0158] Desktop version can be installed on PCs as well as laptops.
The desktop application can work in stand-alone mode. The data can
be extracted from a relational database or flat files into the
desktop PC so that the user can work on the data independently
without connecting to the corporate data warehouse or data mart.
The administrator of this application just needs to plug the model
into the application and this model is then available to the end
user for his analysis needs.
[0159] The web verison of the product gives access to users from
any location within the company via the intranet or even over the
internet. Any version updates can be replicated for all users by
updating the application only at the server, thereby eliminating
the need for version updates at different user locations. Also the
model can be plugged into the application only on the application
server and the model is then available to all users for their
analysis needs. The web verison can extract data from XML files
apart from any relational database or flat files.
[0160] Currently this application architecture contains two layers.
[0161] Application process layer--actual process layer [0162] Model
building layer--external (plug-in)
[0163] Application process layer contains the following tiers
[0164] Client tier [0165] Web/business tier [0166] Information
tier
[0167] The model building layer performs the following processes
[0168] Data preparation [0169] Data exploration [0170] Model
generation [0171] Model interpretation [0172] Model and MainDataSet
deployment
[0173] This architecture is highly modularized for easy
maintenance. The desktop version of the product contains the
following modules. [0174] Authentication module [0175] Control
module [0176] GUI module [0177] Model module [0178] Visualization
module [0179] Apps module [0180] Criteria module [0181] Exception
module [0182] Metapack module [0183] Util module
[0184] A data mining application is used for prediction, analysis
and visualization. It uses algorithm models built by using data
mining tools such as oracle Darwin, SAS E-miner, SPSS Clementine.
These models are `plugged` into the application and used for
prediction, analysis and visualization. The product comes in two
versions, one for desktop users and another web based version.
[0185] There are several data mining applications available in the
market. But the primary deficiency is that it does not
differentiate between expert users and the ordinary business users.
The data modeling component involving complex statistical
techniques and the query and visualization component, which helps
in interpreting the results are tied together, thereby making the
analysis a difficult proposition for the ordinary users.
[0186] Off-line analysing and processing involves providing for
information anytime, anywhere. It is an application, which provides
for multidimensional analysis of the data in stand-alone mode
without connecting to the server, transmission of reports via
multiple communication channels (push mechanism) to the user and
sharing of analytical business data with business partners without
compromising on security.
[0187] From the foregoing description, it should be undestood that
the description is made by way of example only and that the
invention should not be understood as limited to the particular
embodiments described herein. It is also to be understood that
various modifications, rearrangements and substitutions can be made
by one skilled in the art without departing from the scope and
spirit of the invention.
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